To achieve better results in the diagnosis, treatment and monitoring of liver diseases, such as liver failure due to cirrhosis and liver cancer (both primary and secondary), an accurate segmentation of liver structures (or liver objects) in medical images, such as CT images, is required. The liver objects may be for example, the liver itself, a tumor in the liver, vasculature structure in the liver (i.e. liver vessels) or functional segments of the liver.
Manual segmentation of liver structures in CT images is not only time consuming, it also involves large inter- and intra-expert variations. Although automatic segmentation methods may be adopted instead, these automatic segmentation methods are typically based on various expected properties of images or structures and may thus fail to produce sufficiently accurate results when the properties of the test images or structures deviate from the expected properties. Alternatively, interactive methods may be employed. However, most interactive methods have their limitations in their applicability to segmenting medical images in a clinical setting. For example, they may be sensitive to how numerous parameters or initial positions of certain points are set. Furthermore, when using interactive methods, a large amount of user interaction may be required to keep track of the topology of a structure, or to handle structures comprising small complex details.
At present, many liver surgical planning prototype systems [1, 2, 3] mainly employ user-visualization and user-interaction for a subjective or qualitative assessment of liver objects and there has been no extensive investigation on how to perform reliable and efficient liver object segmentation. For example, although MeVis [4] provides a service for liver object segmentation, it is almost impossible to use this service for emergency cases. This is because to use this service provided by MeVis, the hospitals are required to send the CT scans to MeVis, and for each case, several days are required before the liver object segmentation results can be obtained.